ArticleGigaScience2018
Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa.
Article in GigaScience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
35 citing papers in PubMed, 58 citations in OpenAlex.
- Screening of exogenous nutrients for pathogenic bacteria and development of highly active bactericides.mSystems · 2026Article
- An integrated systems biology approach establishes arginine biosynthesis as a metabolic weakness in Candida albicans during host infection.Cell communication and signaling : CCS · 2025Article
- Perturbation ofmBio · 2025Article
- Sustainable software development in science - insights from 20 years of Vanted.Journal of integrative bioinformatics · 2025Article
- Metabolic model guided CRISPRi identifies a central role for phosphoglycerate mutase inmSystems · 2024Article
- Preserving the efficacy of antibiotics to tackle antibiotic resistance.Microbial biotechnology · 2024Review
- Antibacterial activity and mechanism of luteolin isolated fromFrontiers in pharmacology · 2024Article
- Genomic variations in polymyxin-resistant Pseudomonas aeruginosa clinical isolates and their effects on polymyxin resistance.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Article
- Genome-scale model of Pseudomonas aeruginosa metabolism unveils virulence and drug potentiation.Communications biology · 2023Article
- Towards a hybrid user interface for the visual exploration of large biomolecular networks using virtual reality.Journal of integrative bioinformatics · 2022Article
- Systems Biology: New Insight into Antibiotic Resistance.Microorganisms · 2022Review
- Integrated metabolomic and transcriptomic analyses of the synergistic effect of polymyxin-rifampicin combination against Pseudomonas aeruginosa.Journal of biomedical science · 2022Article
- Design considerations for representing systems biology information with the Systems Biology Graphical Notation.Journal of integrative bioinformatics · 2022Article
- Genome-scale metabolic modeling in antimicrobial pharmacology.Engineering microbiology · 2022Review
- A curated collection ofGenome research · 2022Article
- Dissecting carbon metabolism ofComputational and structural biotechnology journal · 2022Article
- Bacterial metabolism and pathogenesis intimate intertwining: time for metabolic modelling to come into action.Microbial biotechnology · 2022Article
- Optimizing Antimicrobial Therapy by Integrating Multi-Omics With Pharmacokinetic/Pharmacodynamic Models and Precision Dosing.Frontiers in pharmacology · 2022Review
- Article
- Path to improving the life cycle and quality of genome-scale models of metabolism.Cell systems · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors at 3 institutions in 2 countries.
Funding
Abstract
Background: Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. Findings: A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Conclusions: Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.